Description Usage Arguments Details Value References
Default windows, strategy and smoothing functions used for portfolio backtesting.
1 2 3 4 5 6  equidistWindows(data, backtest = portfolioBacktest())
tangencyStrategy(data, spec = portfolioSpec(), constraints = "LongOnly",
backtest = portfolioBacktest())
emaSmoother(weights, spec, backtest)

data 
a multivariate time series described by an S4 object of class

backtest 
an S4 object of class 
spec 
an S4 object of class 
constraints 
a character string vector, containing the constraints of the form 
weights 
a numeric vector, containing the portfolio weights of an asset 
equidistWindows:
Defines equal distant rolling windows.
The function requires two arguments: data
and
backtest
, see above. To assign the horizon
value to the backtest specification structure, use the function
setWindowsHorizon
.
tangencyStrategy:
A predefined tangency portfolio strategy.
The function requires four arguments: data
, spec
,
constraints
and backtest
, see above.
emaSmoother:
A predefined weights smoother (EMA) for portfolio backtesting.
The function requires three arguments: weights
, spec
and backtest
, see above. To assign initial starting weights,
smoothing parameter (lambda) or whether to perform double smoothing
to the backtest specification structure, use the functions
setSmootherInitialWeights
, setSmootherLambda
and setSmootherDoubleSmoothing
, respectively.
equidistWindows
function returns the "from" and "to" dates of the rolling window
in a list form.
tangencyStrategy
function returns a S4 object of class "fPORTFOLIO"
.
emaSmoother
function returns a numeric vector of smoothed weights.
W\"urtz, D., Chalabi, Y., Chen W., Ellis A. (2009); Portfolio Optimization with R/Rmetrics, Rmetrics eBook, Rmetrics Association and Finance Online, Zurich.
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